Classification and regression are one of the most common words one must have heard if interested in machine learning or has been working in the same. But there is one more innovative technique known as similarity problems which finds if two inputs are similar or not which is known as a siamese neural network. This kind of neural network architecture is scalable and does not require much retraining. I assume the readers are familiar with CNN for image classification and have trained in an image classification model or normal classification type before, where one must have a trained model e.g. a model that can recognize images of dogs and cats using normal deep learning networks or fully connected layer network. At end of this article, one will get a clear understanding of siamese network architecture, its loss functions, and its application, and will implement an end-to-end model using siamese networks.

In this tutorial you will learn how to implement and train siamese networks using Keras, TensorFlow, and Deep Learning. Practical, real-world use cases of siamese networks include face recognition, signature verification, prescription pill identification, and more! Furthermore, siamese networks can be trained with astoundingly little data, making more advanced applications such as one-shot learning and few-shot learning possible. To learn how to implement and train siamese networks with Keras and TenorFlow, just keep reading. In the first part of this tutorial, we will discuss siamese networks, how they work, and why you may want to use them in your own deep learning applications. From there, you'll learn how to configure your development environment such that you can follow along with this tutorial and learn how to train your own siamese networks.